Application of Machine Learning in the Field of Diabetes

Abstract

Diabetes, a prevalent chronic disease worldwide, poses significant challenges to healthcare systems, with its cases steadily increasing. Machine learning has emerged as a promising tool in diabetes research, offering opportunities for prediction and diagnosis through analysis of vast clinical datasets. This paper systematically reviews and analyzes the latest research progress in ML applications for diabetes, encompassing risk prediction and clinical diagnosis. Various ML techniques, including neural networks, supervised learning, semi-supervised learning, deep learning, and ensemble learning, are explored in their application to diabetes research. While ML has shown promise in improving diagnostic accuracy and efficiency, several challenges remain, such as data quality, model interpretability, and computational requirements. Recommendations for future research focus on addressing these challenges to further advance ML’s effectiveness in diabetes management. Through this review, we aim to provide valuable insights for researchers and clinicians, promoting the continued development and application of ML technology in diabetes care, improving efficiency and accuracy.